Python Tools in Computational Chemistry (And Biology)
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Free and Open Source Software for Computational Chemistry Education
Free and Open Source Software for Computational Chemistry Education Susi Lehtola∗,y and Antti J. Karttunenz yMolecular Sciences Software Institute, Blacksburg, Virginia 24061, United States zDepartment of Chemistry and Materials Science, Aalto University, Espoo, Finland E-mail: [email protected].fi Abstract Long in the making, computational chemistry for the masses [J. Chem. Educ. 1996, 73, 104] is finally here. We point out the existence of a variety of free and open source software (FOSS) packages for computational chemistry that offer a wide range of functionality all the way from approximate semiempirical calculations with tight- binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, both for molecular and for solid-state systems. By their very definition, FOSS packages allow usage for whatever purpose by anyone, meaning they can also be used in industrial applications without limitation. Also, FOSS software has no limitations to redistribution in source or binary form, allowing their easy distribution and installation by third parties. Many FOSS scientific software packages are available as part of popular Linux distributions, and other package managers such as pip and conda. Combined with the remarkable increase in the power of personal devices—which rival that of the fastest supercomputers in the world of the 1990s—a decentralized model for teaching computational chemistry is now possible, enabling students to perform reasonable modeling on their own computing devices, in the bring your own device 1 (BYOD) scheme. In addition to the programs’ use for various applications, open access to the programs’ source code also enables comprehensive teaching strategies, as actual algorithms’ implementations can be used in teaching. -
Real-Time Pymol Visualization for Rosetta and Pyrosetta
Real-Time PyMOL Visualization for Rosetta and PyRosetta Evan H. Baugh1, Sergey Lyskov1, Brian D. Weitzner1, Jeffrey J. Gray1,2* 1 Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America, 2 Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America Abstract Computational structure prediction and design of proteins and protein-protein complexes have long been inaccessible to those not directly involved in the field. A key missing component has been the ability to visualize the progress of calculations to better understand them. Rosetta is one simulation suite that would benefit from a robust real-time visualization solution. Several tools exist for the sole purpose of visualizing biomolecules; one of the most popular tools, PyMOL (Schro¨dinger), is a powerful, highly extensible, user friendly, and attractive package. Integrating Rosetta and PyMOL directly has many technical and logistical obstacles inhibiting usage. To circumvent these issues, we developed a novel solution based on transmitting biomolecular structure and energy information via UDP sockets. Rosetta and PyMOL run as separate processes, thereby avoiding many technical obstacles while visualizing information on-demand in real-time. When Rosetta detects changes in the structure of a protein, new coordinates are sent over a UDP network socket to a PyMOL instance running a UDP socket listener. PyMOL then interprets and displays the molecule. This implementation also allows remote execution of Rosetta. When combined with PyRosetta, this visualization solution provides an interactive environment for protein structure prediction and design. Citation: Baugh EH, Lyskov S, Weitzner BD, Gray JJ (2011) Real-Time PyMOL Visualization for Rosetta and PyRosetta. -
A Nano-Visualization Software for Education and Research
A Nano-Visualization Software for Education and Research Lillian C. Oetting Department of Computer Science, Stanford University, Stanford, CA 94305 USA West High School, Iowa City, IA 52246 USA Tehseen Z. Raza Department of Physics and Astronomy, University of Iowa, Iowa City, IA 52242 USA Hassan Raza Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242 USA Centre for Fundamental Research, Islamabad, Pakistan Abstract: We report the development of a user-friendly nano-visualization software program which can acquaint high-school students with nanotechnology. The visual introduction to atoms and molecules, which are the building blocks of this technology, is an effective way to introduce the key concepts in this area. The software’s graphical user interface enables multidimensional atomic visualization by using ball and stick schematics. Additionally, the software provides the option of wavefunction visualization for arbitrary nanomaterials and nanostructures by using extended Hückel theory. The software is instructive, application oriented and may be useful not only in high school education but also for the undergraduate research and teaching. 1 I. Introduction: The ability to accurately depict atomic, molecular and electronic structures has been a key factor in the advancement of nanotechnology. In this context, it is imperative to provide teaching and research platforms to motivate students towards this novel technology [1-3], while keeping the societal implications in perspective [4]. Nano-visualization appeals well due to its simplistic, yet elegant approach towards the visual representation of detailed concepts about quantum mechanics, quantum chemistry and linear algebra. Additionally, the conflux of quantum mechanics, numerical computation, graphical design, and computer programming gives exposure to the multi-disciplinary aspect of this technology [5-8]. -
User Manual for the Uppsala Quantum Chemistry Package UQUANTCHEM V.35
User manual for the Uppsala Quantum Chemistry package UQUANTCHEM V.35 by Petros Souvatzis Uppsala 2016 Contents 1 Introduction 6 2 Compiling the code 7 3 What Can be done with UQUANTCHEM 9 3.1 Hartree Fock Calculations . 9 3.2 Configurational Interaction Calculations . 9 3.3 M¨ollerPlesset Calculations (MP2) . 9 3.4 Density Functional Theory Calculations (DFT)) . 9 3.5 Time Dependent Density Functional Theory Calculations (TDDFT)) . 10 3.6 Quantum Montecarlo Calculations . 10 3.7 Born Oppenheimer Molecular Dynamics . 10 4 Setting up a UQANTCHEM calculation 12 4.1 The input files . 12 4.1.1 The INPUTFILE-file . 12 4.1.2 The BASISFILE-file . 13 4.1.3 The BASISFILEAUX-file . 14 4.1.4 The DENSMATSTARTGUESS.dat-file . 14 4.1.5 The MOLDYNRESTART.dat-file . 14 4.1.6 The INITVELO.dat-file . 15 4.1.7 Running Uquantchem . 15 4.2 Input parameters . 15 4.2.1 CORRLEVEL ................................. 15 4.2.2 ADEF ..................................... 15 4.2.3 DOTDFT ................................... 16 4.2.4 NSCCORR ................................... 16 4.2.5 SCERR .................................... 16 4.2.6 MIXTDDFT .................................. 16 4.2.7 EPROFILE .................................. 16 4.2.8 DOABSSPECTRUM ............................... 17 4.2.9 NEPERIOD .................................. 17 4.2.10 EFIELDMAX ................................. 17 4.2.11 EDIR ..................................... 17 4.2.12 FIELDDIR .................................. 18 4.2.13 OMEGA .................................... 18 2 CONTENTS 3 4.2.14 NCHEBGAUSS ................................. 18 4.2.15 RIAPPROX .................................. 18 4.2.16 LIMPRECALC (Only openmp-version) . 19 4.2.17 DIAGDG ................................... 19 4.2.18 NLEBEDEV .................................. 19 4.2.19 MOLDYN ................................... 19 4.2.20 DAMPING ................................... 19 4.2.21 XLBOMD .................................. -
An Explicit-Solvent Conformation Search Method Using Open Software
An explicit-solvent conformation search method using open software Kari Gaalswyk and Christopher N. Rowley Department of Chemistry, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada ABSTRACT Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics (REMD) to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules a-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different. Subjects Biophysics, Pharmacology, Computational Science Keywords Conformation search, Explicit solvent, Cluster analysis, Replica exchange molecular dynamics INTRODUCTION Many molecules can exist in multiple conformational isomers. Conformational isomers have the same chemical bonds, but differ in their 3D geometry because they hold different torsional angles (Crippen & Havel, 1988). The conformation of a molecule can affect Submitted 5 April 2016 chemical reactivity, molecular binding, and biological activity (Struthers, Rivier & Accepted 6May2016 Published 31 May 2016 Hagler, 1985; Copeland, 2011). Conformations differ in stability because they experience different steric, electrostatic, and solute-solvent interactions. The probability, p,ofa Corresponding author Christopher N. -
Computational Chemistry for Chemistry Educators Shawn C
Volume 1, Issue 1 Journal Of Computational Science Education Computational Chemistry for Chemistry Educators Shawn C. Sendlinger Clyde R. Metz North Carolina Central University College of Charleston Department of Chemistry Department of Chemistry and Biochemistry 1801 Fayetteville Street, 66 George Street, Durham, NC 27707 Charleston, SC 29424 919-530-6297 843-953-8097 [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we describe an ongoing project where the goal is to The majority of today’s students are technologically savvy and are develop competence and confidence among chemistry faculty so often more comfortable using computers than the faculty who they are able to utilize computational chemistry as an effective teach them. In order to harness the student’s interest in teaching tool. Advances in hardware and software have made technology and begin to use it as an educational tool, most faculty research-grade tools readily available to the academic community. members require some level of instruction and training. Because Training is required so that faculty can take full advantage of this chemistry research increasingly utilizes computation as an technology, begin to transform the educational landscape, and important tool, our approach to chemistry education should reflect attract more students to the study of science. this. The ability of computer technology to visualize and manipulate objects on an atomic scale can be a powerful tool to increase both student interest in chemistry as well as their level of Categories and Subject Descriptors understanding. Computational Chemistry for Chemistry Educators (CCCE) is a project that seeks to provide faculty the J.2 [Physical Sciences and Engineering]: Chemistry necessary knowledge, experience, and technology access so that they can begin to design and incorporate computational approaches in the courses they teach. -
Chemdoodle Web Components: HTML5 Toolkit for Chemical Graphics, Interfaces, and Informatics Melanie C Burger1,2*
Burger. J Cheminform (2015) 7:35 DOI 10.1186/s13321-015-0085-3 REVIEW Open Access ChemDoodle Web Components: HTML5 toolkit for chemical graphics, interfaces, and informatics Melanie C Burger1,2* Abstract ChemDoodle Web Components (abbreviated CWC, iChemLabs, LLC) is a light-weight (~340 KB) JavaScript/HTML5 toolkit for chemical graphics, structure editing, interfaces, and informatics based on the proprietary ChemDoodle desktop software. The library uses <canvas> and WebGL technologies and other HTML5 features to provide solutions for creating chemistry-related applications for the web on desktop and mobile platforms. CWC can serve a broad range of scientific disciplines including crystallography, materials science, organic and inorganic chemistry, biochem- istry and chemical biology. CWC is freely available for in-house use and is open source (GPL v3) for all other uses. Keywords: ChemDoodle Web Components, Chemical graphics, Animations, Cheminformatics, HTML5, Canvas, JavaScript, WebGL, Structure editor, Structure query Introduction Mobile browsers did support HTML5, which opened How we communicate chemical information is increas- the door to web applications built with only HTML, ingly technology driven. Learning management systems, CSS and JavaScript (JS), such as the ChemDoodle Web virtual classrooms and MOOCs are a few examples where Components. chemistry educators need forward compatible tools for digital natives. Companies that implement emerg- Review ing web technologies can find efficiencies and benefit The ChemDoodle Web Components technology stack from competitive advantages. The first chemical graph- and features ics toolkit for the web, MDL Chime, was introduced in The ChemDoodle Web Components library, released in 1996 [1]. Based on the molecular visualization program 2009, is the first chemistry toolkit for structure viewing RasMol, Chime was developed as a plugin for Netscape and editing that is originally built using only web stand- and later for Internet Explorer and Firefox. -
Preparing and Analyzing Large Molecular Simulations With
Preparing and Analyzing Large Molecular Simulations with VMD John Stone, Senior Research Programmer NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign VMD Tutorials Home Page • http://www.ks.uiuc.edu/Training/Tutorials/ – Main VMD tutorial – VMD images and movies tutorial – QwikMD simulation preparation and analysis plugin – Structure check – VMD quantum chemistry visualization tutorial – Visualization and analysis of CPMD data with VMD – Parameterizing small molecules using ffTK Overview • Introduction • Data model • Visualization • Scripting and analytical features • Trajectory analysis and visualization • High fidelity ray tracing • Plugins and user-extensibility • Large system analysis and visualization VMD – “Visual Molecular Dynamics” • 100,000 active users worldwide • Visualization and analysis of: – Molecular dynamics simulations – Lattice cell simulations – Quantum chemistry calculations – Cryo-EM densities, volumetric data • User extensible scripting and plugins • http://www.ks.uiuc.edu/Research/vmd/ Cell-Scale Modeling MD Simulation VMD – “Visual Molecular Dynamics” • Unique capabilities: • Visualization and analysis of: – Trajectories are fundamental to VMD – Molecular dynamics simulations – Support for very large systems, – “Particle” systems and whole cells now reaching billions of particles – Cryo-EM densities, volumetric data – Extensive GPU acceleration – Quantum chemistry calculations – Parallel analysis/visualization with MPI – Sequence information MD Simulations Cell-Scale -
Kepler Gpus and NVIDIA's Life and Material Science
LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc. -
Instructions for PDB Downloading (From Either Website)
Molecular Visualization A BBSI Tutorial http://www.ccbb.pitt.edu/BBSI/index.htm By: Jeffry D. Madura Joshua A. Plumley Thomas J. Dick Table of Contents Instructions for PDB downloading..........................................................2 RasMol Tutorial ...........................................................................................3 VMD Tutorial ...............................................................................................5 CAChe Tutuorial..........................................................................................8 MOE Tutorial ............................................................................................. 10 Chimera Tutorial ....................................................................................... 13 Exercises .................................................................................................... 16 1 Instructions for PDB downloading (from either website) -Go to: - Go to: www.rcsb.org/pdb/ www.pdb.bu.edu/oca-bin/pdblite -Type in name of protein (examples at - Type in name of Protein / bottom of the page). Macromolecule ( examples at bottom of page). - Click on Name icon (first name in purple box). - Click on Retrieve Released Data Matching Your Query icon. -On the left side of the screen, click on Download/Display Structure - Highlight any of the molecule name and click on the View/ Analyze/ -Under Download the Structure File, Save Macro Molecule icon. right click on the X where the PDB(top) meets with none, under - Under the Data Retrieval section, -
USER MANUAL Version 4.5
GROMACS Groningen Machine for Chemical Simulations USER MANUAL Version 4.5 GROMACS USER MANUAL Version 4.5 Written by Emile Apol, Rossen Apostolov, Herman J.C. Berendsen, Aldert van Buuren, Par¨ Bjelkmar, Rudi van Drunen, Anton Feenstra, Gerrit Groenhof, Peter Kasson, Per Larsson, Peiter Meulenhoff, Teemu Murtola, Szilard´ Pall,´ Sander Pronk, Roland Schultz, Michael Shirts, Alfons Sijbers, Peter Tieleman Berk Hess, David van der Spoel, and Erik Lindahl. Additional contributions by Mark Abraham, Christoph Junghans, Carsten Kutzner, Justin A. Lemkul, Erik Marklund, Maarten Wolf. c 1991–2000: Department of Biophysical Chemistry, University of Groningen. Nijenborgh 4, 9747 AG Groningen, The Netherlands. c 2001–2010: The GROMACS development teams at the Royal Institute of Technology and Uppsala University, Sweden. More information can be found on our website: www.gromacs.org. iv Preface & Disclaimer This manual is not complete and has no pretention to be so due to lack of time of the contributors – our first priority is to improve the software. It is worked on continuously, which in some cases might mean the information is not entirely correct. Comments are welcome, please send them by e-mail to [email protected], or to one of the mailing lists (see www.gromacs.org). We try to release an updated version of the manual whenever we release a new version of the soft- ware, so in general it is a good idea to use a manual with the same major and minor release number as your GROMACS installation. Any revision numbers (like 3.1.1) are however independent, to make it possible to implement bug fixes and manual improvements if necessary. -
Pyscf: the Python-Based Simulations of Chemistry Framework
PySCF: The Python-based Simulations of Chemistry Framework Qiming Sun∗1, Timothy C. Berkelbach2, Nick S. Blunt3,4, George H. Booth5, Sheng Guo1,6, Zhendong Li1, Junzi Liu7, James D. McClain1,6, Elvira R. Sayfutyarova1,6, Sandeep Sharma8, Sebastian Wouters9, and Garnet Kin-Lic Chany1 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena CA 91125, USA 2Department of Chemistry and James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA 3Chemical Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA 4Department of Chemistry, University of California, Berkeley, California 94720, USA 5Department of Physics, King's College London, Strand, London WC2R 2LS, United Kingdom 6Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA 7Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, P. R. China 8Department of Chemistry and Biochemistry, University of Colorado Boulder, Boulder, CO 80302, USA 9Brantsandpatents, Pauline van Pottelsberghelaan 24, 9051 Sint-Denijs-Westrem, Belgium arXiv:1701.08223v2 [physics.chem-ph] 2 Mar 2017 Abstract PySCF is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, so as to facilitate new method development and enable flexible ∗[email protected] [email protected] 1 computational workflows. The package provides a wide range of tools to support simulations of finite-size systems, extended systems with periodic boundary conditions, low-dimensional periodic systems, and custom Hamiltonians, using mean-field and post-mean-field methods with standard Gaussian basis functions. To ensure ease of extensibility, PySCF uses the Python language to implement almost all of its features, while computationally critical paths are implemented with heavily optimized C routines.